Update Numba docs links.

docs/cudf/source/user_guide/cupy-interop.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "2. We can also use `DataFrame.values`.\n",
     "\n",
-    "3. We can also convert via the [CUDA array interface](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html) by using cuDF's `to_cupy` functionality."
+    "3. We can also convert via the [CUDA array interface](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html) by using cuDF's `to_cupy` functionality."
    ]
   },
   {

docs/cudf/source/user_guide/guide-to-udfs.ipynb

@@ -446,8 +446,8 @@
    "id": "00914f2a",
    "metadata": {},
    "source": [
-    "In addition to the Series.apply() method for performing custom operations, you can also pass Series objects directly into [CUDA kernels written with Numba](https://numba.pydata.org/numba-doc/latest/cuda/kernels.html).\n",
-    "Note that this section requires basic CUDA knowledge. Refer to [numba's CUDA documentation](https://numba.pydata.org/numba-doc/latest/cuda/index.html) for details.\n",
+    "In addition to the Series.apply() method for performing custom operations, you can also pass Series objects directly into [CUDA kernels written with Numba](https://numba.readthedocs.io/en/stable/cuda/kernels.html).\n",
+    "Note that this section requires basic CUDA knowledge. Refer to [numba's CUDA documentation](https://numba.readthedocs.io/en/stable/cuda/index.html) for details.\n",
     "\n",
     "The easiest way to write a Numba kernel is to use `cuda.grid(1)` to manage thread indices, and then leverage Numba's `forall` method to configure the kernel for us. Below, define a basic multiplication kernel as an example and use `@cuda.jit` to compile it."
    ]
@@ -485,7 +485,7 @@
    "source": [
     "This kernel will take an input array, multiply it by a configurable value (supplied at runtime), and store the result in an output array. Notice that we wrapped our logic in an `if` statement. Because we can launch more threads than the size of our array, we need to make sure that we don't use threads with an index that would be out of bounds. Leaving this out can result in undefined behavior.\n",
     "\n",
-    "To execute our kernel, must pre-allocate an output array and leverage the `forall` method mentioned above. First, we create a Series of all `0.0` in our DataFrame, since we want `float64` output. Next, we run the kernel with `forall`. `forall` requires us to specify our desired number of tasks, so we'll supply in the length of our Series (which we store in `size`). The [__cuda_array_interface__](https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html) is what allows us to directly call our Numba kernel on our Series."
+    "To execute our kernel, must pre-allocate an output array and leverage the `forall` method mentioned above. First, we create a Series of all `0.0` in our DataFrame, since we want `float64` output. Next, we run the kernel with `forall`. `forall` requires us to specify our desired number of tasks, so we'll supply in the length of our Series (which we store in `size`). The [__cuda_array_interface__](https://numba.readthedocs.io/en/stable/cuda/cuda_array_interface.html) is what allows us to directly call our Numba kernel on our Series."
    ]
   },
   {
@@ -2624,7 +2624,7 @@
     "- Generalized NA UDFs\n",
     "\n",
     "\n",
-    "For more information please see the [cuDF](https://docs.rapids.ai/api/cudf/nightly/), [Numba.cuda](https://numba.pydata.org/numba-doc/dev/cuda/index.html), and [CuPy](https://docs-cupy.chainer.org/en/stable/) documentation."
+    "For more information please see the [cuDF](https://docs.rapids.ai/api/cudf/nightly/), [Numba.cuda](https://numba.readthedocs.io/en/stable/cuda/index.html), and [CuPy](https://docs-cupy.chainer.org/en/stable/) documentation."
    ]
   }
  ],

python/cudf/cudf/core/dataframe.py

@@ -3942,7 +3942,7 @@ def apply(
         ``apply`` relies on Numba to JIT compile ``func``.
         Thus the allowed operations within ``func`` are limited to `those
         supported by the CUDA Python Numba target
-        <https://numba.pydata.org/numba-doc/latest/cuda/cudapysupported.html>`__.
+        <https://numba.readthedocs.io/en/stable/cuda/cudapysupported.html>`__.
         For more information, see the `cuDF guide to user defined functions
         <https://docs.rapids.ai/api/cudf/stable/user_guide/guide-to-udfs.html>`__.
 
@@ -4297,7 +4297,7 @@ def apply_chunks(
         respectively (See `numba CUDA kernel documentation`_).
 
         .. _numba CUDA kernel documentation:\
-        http://numba.pydata.org/numba-doc/latest/cuda/kernels.html
+        https://numba.readthedocs.io/en/stable/cuda/kernels.html
 
         In the example below, the *kernel* is invoked concurrently on each
         specified chunk. The *kernel* computes the corresponding output